Upstream Machine Learning in Radiology - 22/10/21

Doi : 10.1016/j.rcl.2021.07.009

Christopher M. Sandino, PhD ^a, Elizabeth K. Cole, MS ^a, Cagan Alkan, MS ^a, Akshay S. Chaudhari, PhD ^b,^c, Andreas M. Loening, MD, PhD ^c, Dongwoon Hyun, PhD ^c,^{^{1
Present address: 3155 Porter Drive, Palo Alto, CA 94304.}}, Jeremy Dahl, PhD ^c,^{^{1
Present address: 3155 Porter Drive, Palo Alto, CA 94304.}}, Abdullah-Al-Zubaer Imran, PhD ^c, Adam S. Wang, PhD ^c, Shreyas S. Vasanawala, MD, PhD ^c,^⁎
^a Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA
^b Department of Biomedical Data Science, 1201 Welch Road, Stanford, CA 94305, USA
^c Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA

^∗Corresponding author.

Resumen

Machine learning (ML) and Artificial intelligence (AI) has the potential to dramatically improve radiology practice at multiple stages of the imaging pipeline. Most of the attention has been garnered by applications focused on improving the end of the pipeline: image interpretation. However, this article reviews how AI/ML can be applied to improve upstream components of the imaging pipeline, including exam modality selection, hardware design, exam protocol selection, data acquisition, image reconstruction, and image processing. A breadth of applications and their potential for impact is shown across multiple imaging modalities, including ultrasound, computed tomography, and MRI.

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Keywords : Deep learning, Artificial intelligence, Medical imaging, Image reconstruction